version: 0.2

phases:
  install:
    runtime-versions:
      python: 3.8
    commands:
      - pip install --upgrade --force-reinstall . "awscli>1.20.30"

  build:
    commands:
      - export PYTHONUNBUFFERED=TRUE
      - export SAGEMAKER_PROJECT_NAME_ID="${SAGEMAKER_PROJECT_NAME}-${SAGEMAKER_PROJECT_ID}"
      # Copy sample dataset for template - REMOVE when using your own data
      - aws s3 cp s3://sagemaker-sample-files/datasets/tabular/uci_abalone/abalone.csv .
      - aws s3 cp abalone.csv s3://${ARTIFACT_BUCKET}
      - |
        run-pipeline --module-name ml_pipelines.training.pipeline \
          --role-arn $SAGEMAKER_PIPELINE_ROLE_ARN \
          --tags "[{\"Key\":\"sagemaker:project-name\", \"Value\":\"${SAGEMAKER_PROJECT_NAME}\"}, {\"Key\":\"sagemaker:project-id\", \"Value\":\"${SAGEMAKER_PROJECT_ID}\"}]" \
          --kwargs "{\"region\":\"${AWS_REGION}\",\"role\":\"${SAGEMAKER_PIPELINE_ROLE_ARN}\",\"default_bucket\":\"${ARTIFACT_BUCKET}\",\"pipeline_name\":\"${SAGEMAKER_PROJECT_NAME_ID}\",\"model_package_group_name\":\"${MODEL_PACKAGE_GROUP_NAME}\",\"base_job_prefix\":\"${SAGEMAKER_PROJECT_NAME_ID}\", \"bucket_kms_id\":\"${ARTIFACT_BUCKET_KMS_ID}\", \"git_hash\":\"${CODEBUILD_RESOLVED_SOURCE_VERSION}\",  \"ecr_repo_uri\":\"${ECR_REPO_URI}\",  \"default_input_data\":\"s3://${ARTIFACT_BUCKET}/abalone.csv\"}"
      - echo "Create/Update of the SageMaker Pipeline and execution completed."